AI enabled

Data Analytics and Gen AI Certification Program

Master Excel, Power BI, SQL, Python, statistics, machine learning basics, and applied GenAI workflows through a practical programme built for UK data analyst roles.

Cohort: Starting Soon
Duration: 6 months
Emma ThompsonJames WalkerMeera Iyer
10,000+ Trained

Learner Reviews

4.9

Based on 42 first-party learner reviews

These ratings come from learner feedback published on this page and are mirrored in the course structured data for search engines.

Career Outcomes

Target Roles
  • Data Analyst
  • Business Analyst
  • Financial Analytics
  • Healthcare Analytics
Average Salary (UK)
£28,000 – £55,000+

Post-completion

Industry Demand
  • Finance
  • Retail
  • SaaS
  • Consulting
  • IT
  • Healthcare

Is This Right Fit For You?

Non-tech professionals switching to data analytics
Beginners starting a data analytics career
Excel users upgrading to Power BI, SQL, Python, and AI-enabled workflows
Anyone searching for a data analytics course UK with job-focused projects

Curriculum

The full 26-week Data Analytics with AI curriculum is expanded below, including phase-by-phase topics, weekly labs, AI integration, and portfolio outcomes.

View detailed curriculum

Overview

Duration6 months
FormatLive + hands-on
LevelBeginner-friendly

Course Investment

£3,499
EMI Available

Tools Covered

Excel
Power BI
SQL
Python
pandas
scikit-learn
OpenAI API
GitHub

UK job-ready certification plan

Data Analytics and Applied AI Certification

A six-month weekend programme built around Excel, Power BI, SQL, Python, statistics, machine learning, and AI-assisted analyst workflows. Learners finish with portfolio evidence for junior data analyst, BI analyst, reporting analyst, operations analyst, and data insight roles.

26 weeks104 guided hours6 portfolio projectsAI in every module

Entry-level readiness

Junior analyst, BI, reporting, operations, and people/data insight roles

Career acceleration

For professionals adding analytics and AI to an existing domain

Responsible AI

Redaction, accountability, transparency, accuracy, fairness, and security

Recommended curriculum balance

Spend the most time where UK analyst demand is strongest.

Excel and statistics stay focused and practical. Power BI, SQL, Python, automation, and AI-enabled workflows get the deeper build time because they create the strongest day-to-day evidence for hiring conversations.

Excel

3 weeks

12 live hours

Analyst essentials for day-one productivity

Power BI

5 weeks

20 live hours

High visibility skill in UK analyst hiring

SQL + Advanced SQL

5 weeks

20 live hours

Querying, joining, transforming, and explaining data

Python + AI Integration

7 weeks

28 live hours

Automation, AI, APIs, and analyst tooling

Statistics

2 weeks

8 live hours

Interpretation for decisions, not academic overload

Machine Learning

4 weeks

16 live hours

Predictive and segmentation literacy for junior roles

Detailed syllabus

Five phases with AI embedded from the first week.

The course is not a separate AI add-on. Learners use AI safely inside Excel, Power BI, SQL, Python, statistics, ML, documentation, and career preparation.

Phase 1

Data Foundations & Visualization

Build a practical foundation in spreadsheet analytics, clean reporting, dashboard thinking, and business storytelling.

Excel for Data Analysis

Weeks 1-3

Project: Operations KPI Tracker
Workbook structure, data types, tables, sorting, filtering, and validation
Cleaning messy spreadsheets, conditional formatting, and reporting standards
IF/IFS, SUMIFS, COUNTIFS, AVERAGEIFS, XLOOKUP, text and date functions
PivotTables, PivotCharts, basic dashboards, and Power Query in Excel
AI integration

Use AI to explain formulas, suggest cleanup steps, draft metric definitions, summarize insights, and write a stakeholder-ready memo after redacting sensitive data.

Power BI

Weeks 4-8

Project: Executive BI Dashboard
Data connections, profiling, Power Query, null/error handling, merge and append
Star schema, relationships, date table, semantic model design, and refresh logic
DAX basics, measures vs calculated columns, CALCULATE, filter context, and time intelligence
KPI design, drillthrough, tooltips, accessibility, publishing, workspaces, and row-level security
GenAI integration

Use AI to draft report requirements, KPI definitions, DAX explanations, dashboard narratives, QA checklists, and stakeholder summaries.

Phase 2

Analytical Tools & Techniques

Move from dashboard users to analysts who can query databases, validate outputs, and explain evidence with statistical reasoning.

SQL and Advanced SQL

Weeks 9-13

Project: SQL Business Case Pack
Database concepts, tables, keys, SELECT, WHERE, ORDER BY, GROUP BY, HAVING, and CASE
Joins, unions, subqueries, CTEs, views, record validation, and data-cleaning SQL
Window functions, ranking, running totals, lag/lead, string functions, and date functions
Optimization basics, indexes, EXPLAIN/ANALYZE, SQL style, and documentation
AI integration

Use AI to draft query skeletons, explain joins, convert business questions into SQL, review readability, and validate results without blindly trusting generated queries.

Statistics and Probability

Weeks 21-22

Project: Decision Memo
Data types, mean, median, mode, variance, spread, distributions, and outliers
Percentiles, probability basics, sampling, bias, confidence intervals, and risk
Hypothesis testing, p-values, statistical significance, and business significance
Correlation vs causation, regression interpretation, and A/B test thinking
AI integration

Use AI to translate statistical output into plain English, check interpretation wording, and prepare stakeholder-facing explanations after learners compute and verify results.

Phase 3

Python, Automation & AI Workflows

Use Python as the analyst automation layer: clean data, work with files and APIs, create notebooks, and build AI-assisted workflows.

Python with AI Integration

Weeks 14-20

Project: Analyst Copilot Mini-App
Python fundamentals, Jupyter notebooks, variables, control flow, functions, file handling, lists, and dictionaries
pandas I/O, filtering, grouping, aggregation, missing values, joins, reshaping, date/time handling, and visualization
Reusable scripts, automation patterns, APIs, JSON, environment variables, and notebook storytelling
OpenAI API basics, prompt design, structured outputs, function calling, embeddings, and document search
AI integration

Build workflows that turn raw data into KPI calculations, structured JSON summaries, SQL helpers, document Q&A, and reviewed business insight outputs.

Phase 4

Applied Machine Learning & Responsible AI

Learn enough ML to frame practical predictive problems, evaluate models, explain limits, and use AI responsibly in analytics work.

Machine Learning Basics

Weeks 23-26

Project: Prediction or Segmentation Prototype
ML workflow, problem framing, supervised vs unsupervised learning, train/test split, and leakage
Preprocessing, encoding, scaling, pipelines, regression, classification, and clustering
Confusion matrix, MAE/RMSE, precision, recall, F1, overfitting, and model comparison
Feature importance, explainability, model bias, model cards, and when not to use ML
AI integration

Use AI to compare algorithms, generate model cards, explain feature trade-offs, summarize model limits, and present results to non-technical stakeholders.

Git, GitHub and AI Governance

Embedded throughout

Project: Portfolio Repository + AI-Use Declaration
Git basics, GitHub repositories, branch workflow, README writing, and project documentation
Prompt templates, structured outputs, retrieval notes, validation logs, and human review checkpoints
Data minimization, redaction, transparency, fairness, security, accuracy, and individual rights
Clear declarations of where AI was used, what was verified, and what limitations remain
AI integration

Learners document AI usage like professionals: data provided, outputs generated, human checks completed, and governance risks still open.

Phase 5

Career Acceleration & Professional Excellence

Convert course work into employability proof with a targeted CV, LinkedIn and GitHub profile, interview practice, and portfolio storytelling.

Career Services and Interview Prep

Final portfolio sprint

Project: Hiring-Ready Portfolio Pack
Resume building, cover letter writing, and role-specific tailoring for UK job descriptions
LinkedIn and GitHub optimization with clear project summaries and documented repositories
Communication skills, interview etiquette, project walkthroughs, and stakeholder explanation practice
Aptitude preparation kit, mock interviews, salary conversation prep, and final portfolio review
AI integration

Use AI to draft and refine role-specific CV bullets, interview answers, LinkedIn summaries, and project narratives while keeping claims accurate and evidence-based.

Week-by-week teaching plan

Saturday concepts, Sunday labs, portfolio progress every week.

Each weekend has a clear rhythm: Saturday introduces the concepts and live demos; Sunday turns that learning into guided labs, project work, and AI-in-workflow practice.

Week
Saturday class
Sunday class
Project milestone
Week 1
UK analyst role, workflow, Excel hygiene
Data types, validation, cleanup, safe AI use
Start Excel project dataset and data dictionary
Week 2
IF/IFS, SUMIFS, COUNTIFS, percentages
XLOOKUP, text/date functions, AI formula review
Build KPI calculation sheet
Week 3
PivotTables, charts, dashboard layout
Power Query in Excel and AI insight memo
Submit Operations KPI Tracker
Week 4
Power BI orientation and connections
Power Query transformations and refresh logic
Start BI dataset cleanup
Week 5
Data modeling, facts, dimensions, relationships
DAX basics, measures, filter context, CALCULATE
Build base semantic model
Week 6
DAX KPIs, time intelligence, trends
Visual best practices, slicers, drillthrough
Add KPI measures and first report page
Week 7
Dashboard storytelling and accessibility
Publishing, refresh, RLS, AI narration QA
Add role-based report and executive summary
Week 8
Power BI guided build lab
Project presentation and feedback
Submit Executive BI Dashboard
Week 9
SQL tables, keys, SELECT, WHERE
GROUP BY, HAVING, aggregates, CASE
Explore case-study schema
Week 10
Joins and business meaning
Subqueries, set operations, validation
Write multi-table business queries
Week 11
CTEs and staged transformations
Window functions, ranks, running totals
Build advanced analysis queries
Week 12
String/date/conditional SQL functions
Views, optimization, indexes, EXPLAIN
Improve and tune one query set
Week 13
AI-assisted SQL and documentation
SQL challenge lab and project review
Submit SQL Business Case Pack
Week 14
Python environment, Jupyter, basics
Functions, loops, collections, file handling
Start Python notebook template
Week 15
pandas CSV/Excel I/O and DataFrames
Missing values, types, grouping, aggregations
Clean analysis-ready dataset
Week 16
Merge, join, concat, reshape, dates
Exploratory charts and story-first commentary
Build exploratory notebook
Week 17
Reusable code and analyst automation
APIs, JSON, environment variables, packages
Automate data pull or report prep
Week 18
AI for analysts and API basics
Structured outputs for reliable JSON insights
Generate structured KPI summary
Week 19
Function calling and tool workflows
Embeddings, semantic search, document Q&A
Add retrieval or tool-calling feature
Week 20
End-to-end analyst copilot build lab
Demo day and feedback
Submit Analyst Copilot Mini-App
Week 21
Data types, spread, distributions, outliers
Probability, sampling, bias, confidence intervals
Begin statistical decision memo
Week 22
Hypothesis testing and significance
Correlation, regression, A/B readout, AI memo QA
Submit Decision Memo
Week 23
ML framing, train/test split, leakage
Preprocessing, encoding, scaling, pipelines
Prepare ML-ready dataset
Week 24
Regression workshop and evaluation
Classification, confusion matrix, precision/recall/F1
Compare two baseline models
Week 25
Clustering and segmentation
Feature importance, explainability, bias
Choose final model and write findings
Week 26
Final ML build lab and model card
Portfolio presentation and interview defense
Submit Prediction or Segmentation Prototype

Portfolio outcomes

Six module projects, one hiring-ready evidence pack.

The certificate should not end with only quizzes. Every module creates a concrete submission that shows practical ability, communication, validation, and responsible AI use.

Operations KPI Tracker

Clean messy files, calculate KPIs, and report clearly

Excel workbook, cleaned tabs, formula sheets, Pivot dashboard, and insight memo

Executive BI Dashboard

Model data, build dashboards, and communicate to stakeholders

PBIX file, semantic model, DAX measures, screenshots or published report, and walkthrough

SQL Business Case Pack

Answer business questions from structured data with readable SQL

.sql scripts, query notes, output screenshots, and tuning note

Analyst Copilot Mini-App

Automate analysis and use AI responsibly in workflows

Notebook or app, prompt templates, structured JSON example, and governance note

Decision Memo

Interpret evidence rather than only calculate formulas

Written recommendation, calculations, charts, and uncertainty explanation

Prediction or Segmentation Prototype

Frame an ML problem, evaluate a model, and explain limits

Notebook, metrics summary, feature explanation, model card, and stakeholder summary

Required submission pattern

Business problem
Data source description
Cleaning log
Analysis steps
Output or dashboard
Recommendation
Limitations
AI-use declaration

Teach Excel tightly

Keep Excel focused on tables, formulas, lookups, PivotTables, dashboards, and Power Query basics rather than adding VBA into the core path.

Make statistics practical

Use statistics for interpretation, evidence, risk, and avoiding misleading recommendations rather than turning it into a theory block.

Keep the stack consistent

Use one SQL dialect, one Python lab stack, GitHub repositories, pandas, scikit-learn, notebooks, and repeatable project documentation.

Simple course promise

Teach the fundamentals just enough, then spend most of the six months on Power BI, SQL, Python, and AI-powered analyst workflows, with a project in every module and a portfolio outcome every month.

Career guides

Read before choosing this programme

These Brit Institute guides explain the UK roles, salaries, tools, and learning path connected to this course.

How to Become

How to Become a Data Analyst in the UK (Step-by-Step Guide)

A practical roadmap for breaking into data analytics — from learning core tools to landing your first role in the UK job market.

Read guide →Salary Insights

Data Analyst Salary in the UK (2026 Guide)

Comprehensive breakdown of data analyst salaries across UK cities, industries, and experience levels.

Read guide →Tools & Skills

Python vs SQL: Which Should Data Analysts Learn First?

A practical comparison to help you decide whether to start with Python or SQL on your data analytics journey.

Read guide →

Hands-On Projects

  • Operations KPI Tracker
  • Executive BI Dashboard
  • SQL Business Case Pack
  • Analyst Copilot Mini-App
  • Decision Memo
  • Prediction or Segmentation Prototype

Career Support

  • CV tailored for UK data analyst jobs
  • Interview prep
  • Portfolio review
  • LinkedIn optimisation
  • Mock interview sessions
  • Targeted job application support

Learner Reviews

4.9 / 5 average from 42 published learner reviews

Priya Sharma

Published 14 Feb 2026

5.0 / 5

The analytics projects felt practical from week one. I used the dashboard work in interviews and moved into a reporting-focused analyst role with much more confidence.

James Okonkwo

Published 29 Jan 2026

5.0 / 5

I joined with Excel experience only and left comfortable with SQL, Tableau, and presenting insights. The mentor feedback on my portfolio was especially useful.

Amina Begum

Published 8 Dec 2025

4.8 / 5

The live support and structured roadmap helped me balance study with work. I could see how each module connected to real analyst tasks in UK job descriptions.

Data Analytics Course UK FAQ

Common questions about studying data analytics in the UK

Is Brit Institute's data analytics course available in the UK?+

Yes. Brit Institute's data analytics course is designed specifically for learners in the UK. The programme is delivered online via live instructor-led sessions, so you can join from anywhere in the United Kingdom.

What is the best data analytics course in the UK for beginners?+

Brit Institute offers a beginner-friendly Data Analytics and Gen AI certification program that covers Excel, Power BI, SQL, Python, statistics, machine learning basics, and applied GenAI workflows from scratch. It includes hands-on projects and end-to-end career support tailored for the UK job market.

How long is the data analytics course at Brit Institute?+

The Data Analytics and Gen AI certification program is 6 months long, with live online classes, recorded sessions, and mentored projects throughout.

What salary can I expect after completing a data analytics course in the UK?+

UK data analyst salaries typically range from £28,000 to £55,000+ depending on experience, location, and sector. Brit Institute graduates have transitioned into roles in finance, retail, SaaS, and consulting.

What tools are taught in the data analytics course UK?+

The course covers Excel, Power BI, SQL, Python, pandas, scikit-learn, OpenAI API workflows, GitHub, and responsible GenAI practices for modern data analyst work.

Do I need a degree to join a data analytics course in the UK?+

No degree is required. Brit Institute's data analytics course is open to career switchers, non-technical professionals, and anyone looking to build data skills for the UK job market.

Does the data analytics course include job placement support?+

Yes. The programme includes CV preparation for UK data analyst roles, interview coaching, portfolio review, and ongoing placement support until you secure a relevant position.

How much does the data analytics course cost in the UK?+

The Data Analytics and Gen AI certification program is priced at £3,499. EMI payment options are available, and eligible learners may qualify for Pay After Placement arrangements.

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